SUMMARY

• Master’s degree with 6+ years of Data Analyst/Scientist industry experience using SAS (6+ years), R (6+ years), SAS EG (5+ years), Python (2+ years), SQL (5+ years) (SQL Server, Oracle and DB2) and MATLAB (3+ years).
• Very Strong (over 10+ years) Quantitative and Statistical background, including Regression Analysis, Econometrics, Machine Learning, Statistical Inference, Optimization Algorithms, Multivariate Statistical Analysis, Advanced Statistical Analysis and Quantitative Analysis.
• Holder of SAS BASE, SAS ADVANCE and SAS BUSINESS ANALYST Certification.
• Familiar with whole process in predictive modelling in Linear Regression, Logistic Regression, ARIMA, Bayesian classifier, K - means clustering and ID3 classification.
• Utilized supervised and unsupervised learning methods of Machine Learning algorithm like K-means Clustering, Classification (Decision Tree and Bayesian Naive Bayes), Association, Principal Component Analysis (PCA), Linear Regression and Logistic Regression.
• Performed stationary test, back-testing, forecast comparison, sensitivity test, residual test.
• Understand knowledge of Hadoop, HDFS (read, write) and experienced in Hive (Mysql).
• Utilized in Data Mining: Data Acquisition, Integrity Check, Analysis of Variance, Detection of Outliers, Missing Value Treatment, Testing Normality and Data Transformation etc.
• Performed optimization algorithms via MATLAB (such as quadprog).
• Performed Anomaly Detection, including Graphical & Statistical-based, Distance-based.
• Very strong Excel experience via Excel and Python (complex transpose and extract data from Excel and output Excel files).
• Data Visualization via business intelligent software Tableau: line plot, scatter plot, bar graph and pie graph.
• Performed ETL process via SAS, including data cleansing and data profiling.
• Ample experience using SAS/BASE: Data Step and Procedures like PROC (SORT, FORMAT, FREQ, REPORT, TABULATE, TRANSPOSE, GLM, MEANS, SGPLOT (GRAPH), REG, UNIVARIATE) etc.
• Ample experience in SAS/PROC SQL: Queries; Inner and Outer Joins; Merging; Inserting Rows; Updating large data sets; removing duplicates; Index application.
• Ample experience in SAS/Macro: flexible use in SAS Macros.
• Strong communication skill & fast learner.
• Able to work on own and good team member.

TECHNICAL SKILLS

Modeling and Statistical Software: SAS (6+), R (6+), SAS EG (5+), Python (2+) and MATLAB (3+).

Microsoft Management: MS Word, Excel, Power Point and MS Outlook

Database Software: Oracle & DB2 & Teradata & Microsoft Server SQL & MS Access

PROFESSIONAL EXPERIENCE

Confidential, Chicago, IL

Credit Risk Quantitative Analyst

Responsibilities:

• Validate more than 30 ARIMA, ARIMAX and Regression predictive models from Texas Capital Bank and Comerica Bank (more than 80 billion capital).
• Lead team to build our own model using clients’ dataset, including raw data manipulation, model assumption test, variable selection, model fitting, model prediction and final model validation.
• Performed model replication, stationary test, in-sample and out-sample back-testing, forecast comparison, sensitivity test, residual test and model fitting analysis.
• Understanding of CCAR, DFAST (stress testing models).
• Conducted SAS coding in SAS 9.4 with ETS, including BASE like DATA (set, merge, keep, drop, rename), ODS (pdf), PROC ARIMA (fitting, forecast), PROC SGPLOT (complex plot), PROC REG; SQL; Macro.
• Test and Debug existing clients’ SAS codes and conduct SAS codes via SAS Enterprise Guide.
• Query, update, insert and delete data via Oracle/DB2 SQL.
• Performed Anomaly (Outlier) Detection, including Graphical, Statistical-based and Distance-based.
• Related knowledge includes T-test, Stationary, ADF test, PP test, Stepwise Selection, R-Square, AIC and White Noise assumption, Difference.
• Performed data handling, data audit, data scrubbing and data classification from raw data.
• Data entry and input via Excel, SQL and SAS.
• Calculate MAPE, MPE, MSE and RMSE via SQL.
• Familiar with building and validating forecast and regression financial models.
• Wrote Model Risk Management Validation report and provided all charts and plots via SAS.

Environment: SAS 9.4, SAS EG, SQL, Share Point, MS Excel and Outlook.

Confidential, Saddle Brook, NJ

Data Scientist

Responsibilities:

• Filtered shoppers’ email from large database via SQL. And provide statistical NLP support for technology team to filter “bad” emails automatically.
• Initiated data mining and analysis activities to explore big data and understand customer’s needs.
• Programed SAS and R via Windows and UNIX system.
• Machine Learning application on text data via Python package regex.
• Built ARIMA model to forecast Total Retail Sales of Consumer Goods. Detail jobs including:
• Data check (Serial Sequence, Stationary, Season and White Noise).
• Bayesian Classifier classify customer behavior via customer profiles:
• Obtain initial customer sample from online.
• Calculate prior purchase probability from sample data.
• Compute posterior purchase probability depending on Bayesian Theorem and prior purchase probability.
• Experienced in Hive (Mysql). And understand knowledge of Hadoop and HDFS (read, write).
• Very strong Excel experience via Excel and Python (complex transpose and extract data from Excel and output Excel files).
• Performed ETL process via SAS, including data cleansing and data profiling.
• Managed several SAS projects via Enterprise Guide.
• Performed data handling, data audit, data scrubbing and data classification from raw data.
• Performed data analysis/analytics and market research in retail area.
• Conducted Statistical modeling rare events in logistic regression (Exact Logistic Regression and Penalized Likelihood Method).
• Data Visualization via business intelligent software Tableau: line plot, scatter plot, bar graph and pie graph.
• Performed non-linear modeling via data transformation.
• Performed optimization via MATLAB (such as quadprog).
• Performed Data Mining: Integrity Check, Detection of Outliers, Testing Normality, Principal Component analysis (PCA), Correlation Analysis and Data Transformation before and when building linear regression and logistic regression model.
• Query, update, insert, delete and merge data via SAS/SQL.
• Forecasting and Predictions with linear regression model, logistic model and ARIMA model.
• Kept connection and coordinated with Marketing and Analytics teams.
• Proficient in MS Word, Excel and PowerPoint (presentation).

Environment: SAS, Python, SAS EG, SQL, Tableau, MATLAB and MS Excel.

Confidential, Short Hills, NJ

Data Analyst

Responsibilities:

• Built Credit Card Scoring System using logistic regression (with more than 2 million rows and 350 variables), the detail jobs including:
• Conducted Detection of Outliers, Testing Normality, Sampling, Principal Component Analysis (PCA) and Data Transformation.
• Manage SAS projects via Enterprise Guide.
• Performed supervised and unsupervised learning methods of Machine Learning algorithm like K-means clustering, Classification (Decision Tree and Bayesian Classifier), Association, Principal Component Analysis (PCA), Linear Regression and Logistic Regression.
• Applied Schemes with Statistical modeling rare events in logistic regression (Exact Logistic Regression and Penalized Likelihood Method).
• Performed Missing Data treatment via three methods.
• Applied Naive Bayesian classifier to classify customers’ willingness for credit card.
• Expertise in SQL scripts to obtain the data we need from datasets (DDL and DML).
• Analyzed and modeled structured data using advanced statistical methods, including linear regression model, logistic regression model, Fama-French model, Black Scholes model, ARIMA model via SAS.
• Proficient in MS Word, Excel and PowerPoint (presentation).

Environment: SAS, Python, SAS Enterprise Guide, MATLAB, MS Excel and SQL.

Confidential, Princeton, NJ

Data Analyst

Responsibilities:

• Obtain data via SQL scripts on DB2 SQL.
• Proficient in MS Word, Excel and PowerPoint (presentation).
• Used client’s data build linear and logistic regression model: check abnormal data, data transformation, first selection run, model selection and model accuracy check. Classify new patients to different categories.
• Used Naive Bayesian classifier to estimate diseases.
• Ensured data reports meet program requirements and are delivered in a timely manner by making necessary changes to improve data accuracy.
• Utilized Machine Learning algorithm like Clustering, Classification (Decision Tree id3 method like Information Gain), Association, Principal Component Analysis (PCA), Linear Regression and Logistic Regression.

Environment: R, SAS, Python, SQL, MS Excel, MS Word and Outlook.

Confidential, NYC, NY

Quantitative Analyst (Part-Time Internship)

Responsibilities:

• Research how to set up option price, using Monte Carlo (Black Scholes model) simulation and FDM in C++.
• Learn different forward options and find the connection between them.
• Perform PCA to build model regress stock price using different independent variables.

Environment: C++, SAS, MATLAB, MS Excel, MS Word and Outlook.

Confidential

Quantitative Analyst (Full-Time Internship)

Responsibilities:

• Used principal component analysis (PCA) to choose stocks from Shanghai Stock Market and make 20 percent profits in two months using SAS.
• Forecasted the economic/financial data using ARIMA model, including Social Retail goods and RMB-HK dollar Foreign Exchange Rate.

Environment: SAS, EViews, MS Excel, MS Word and Outlook.